Abstract
In this paper, an integrated modeling system based on a multi-layer perceptron model is developed and evaluated for the forecasting of urban airborne maximum pollutant concentrations. In the first phase, the multi-objective genetic algorithm (MOGA) and sensitivity analysis are used in combination for identifying feasible system inputs. In the second phase, the final evaluation of the developed system is performed for the concentrations of pollutants measured at an urban air quality station in central Helsinki, Finland. This study showed that the evolutionary design of neural network inputs is an efficient tool, which can help to improve the accuracy of the model. The evaluation work itself showed that the developed modeling system is capable of producing fairly good operational forecasts.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Haykin, S. (1999) Neural Networks: A Comprehensive Foundation, 2nd Edition. Prentice Hall, Upper Saddle River, NJ
Gardner, M. W., Dorling, S.R. (1999) Artificial neural networks (the multi-layer perceptron) — a review of applications in the atmospheric sciences. Atmospheric Environment 32:2627–2636
Ziomas, I. C., Melas, D., Zerefos, C. S., Bais, A. F. (1995) Forecasting peak pollutant levels from meteorological variables. Atmospheric Environment 29:3703–3711
Kukkonen, J., Partanen, L., Karppinen, A., Ruuskanen, J., Junninen, H., Kolehmainen, M, Niska, H., Dorling, S., Chatterton, T., Foxall, R., Cawley, G. (2003) Extensive evaluation of neural networks of NO2 and PM10 concentrations, compared with a deterministic modelling system and measurements in central Helsinki, Atmospheric Environment 37:4539–4550
Kolehmainen, M., Martikainen, H., Ruuskanen, J. (2001) Neural networks and periodic components used in air quality forecasting, Atmospheric Environment 35:815–825
Niska, H., Rantamäki, M., Hiltunen, T., Karppinen, A., Kukkonen, J., Ruuskanen, J., Kolehmainen, M. (2004) Evaluation of the integrated modelling system containing a multi-layer perceptron model and the numerical weahter prediction model HIRLAM for forecasting urban airborne pollutant concentrations. Submitted to Atmospheric Environment.
Goldberg, D. E. (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Publishing Company, Inc.
Fonseca, C. M., Fleming, P. J. (1993) Genetic algorithms for multi-objective optimization: formulation, discussion and generalization. In: Forrest, S. (eds.) 5th International Conference on Genetic Algorithms. Morgan Kauffman, California, pp. 416–423
Deb, K. (1999) Evolutionary Algorithms for Multi-criterion Optimization in Engineering Design, Evolutionary Algorithms in Engineering and Computer Science. John Wiley & Sons Ltd, Chichester
Junninen, H., Niska, H., Tuppurainen, K., Ruuskanen, J., Kolehmainen, M. (2004) Methods for imputation of missing values in air quality data sets. Atmospheric Environment 38:2895–2907
Karppinen, A., Joffre, S., Vaajama, P. (1997) Boundary layer parametrization for Finnish regulatory dispersion models. International Journal of Environment and Pollution 8:557–564
Eerola, K. (2002) The operational HIRLAM at the Finnish Meteorological Institute. HIRLAM Newsletter 41:19–24
Emmanouilidis, C., Hunter, A., MacIntyre, J., Cox, C. (1999) Selecting features in neurofuzzy modelling using multiobjective genetic algorithms. In 9th International Conference on Artificial Neural Networks. Edinburgh, UK, pp. 749–754
Oliveira L.S., Sabourin R., Bortolozzi F., and Suen C.Y. (2003) A methodology for feature selection using multi-objective genetic algorithms for handwritten digit string recognition. International Journal of Pattern Recognition and Artificial Intelligence 17:903–930
Moody J., Utans, J. (1991) Principled architecture selection for neural networks: application to corporate bond rating predictions. Advances in Neural Information Processing Systems 4:683–690
Emmanouilidis, C., Hunter, A., MacIntyre, J. (2000) A multiobjective evolutionary setting for feature selection and a commonality-based crossover operator. In Congress on Evolutionary Computation 2000. IEEE, New Jersey, pp. 309–316
Willmott, C. J. (1981) On the validation of models. Phys. Geog. 2:184–194
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag/Wien
About this paper
Cite this paper
Niska, H., Hiltunen, T., Karppinen, A., Kolehmainen, M. (2005). Evolutionary Design and Evaluation of Modeling System for Forecasting Urban Airborne Maximum Pollutant Concentrations. In: Ribeiro, B., Albrecht, R.F., Dobnikar, A., Pearson, D.W., Steele, N.C. (eds) Adaptive and Natural Computing Algorithms. Springer, Vienna. https://doi.org/10.1007/3-211-27389-1_43
Download citation
DOI: https://doi.org/10.1007/3-211-27389-1_43
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-24934-5
Online ISBN: 978-3-211-27389-0
eBook Packages: Computer ScienceComputer Science (R0)